Opioids.pdf

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Prescription Opioid Misuse and Property Crime

McCaslin Giles,

West Chester University

Michael Malcolm,

1

West Chester University

June 2019

ABSTRACT

While there is an extensive literature on the relationship between drug use and crime, research on crime stemming from the recent uptick in opioid misuse is surprisingly sparse, and much of it provides contradictory answers. Using state-level data and dynamic panel techniques, we find evidence of a large, positive association between nonmedical use of pain relievers and property crime. The correlation is strongest for the youngest users, holds for multiple classes of property crime, and its magnitude suggests hundreds of thousands of excess property crimes over the study period resulting from even modest increases in opioid abuse. We also present evidence of a link between prescribing rates and crime. Evidence of a link between pain relievers and violent crime is much weaker, as is evidence of an association between other drugs and crime. Our use of robust panel inference helps to address endogeneity concerns that can arise in other studies, many of which also draw conclusions from small or nonrepresentative samples.

JEL Classification: I12, I18, K42

Keywords: Pain relievers, theft, crime, opioids

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2 I. Introduction

American pharmacies dispensed more than 191 million prescriptions for opioids in 2016. While down slightly from the record 255 million prescriptions in 2012, the current number is still more than two and a half times the number of prescriptions that were filled in 1991 (CDC 2019; NIH 2014). A substantial part of this opioid use is not for legitimate medical reasons. The NIH (2018) reports that around 2 million Americans abuse prescription opioids, with as many as 29 percent of opioid prescriptions misused. The social costs associated with this problem are growing. Around 90 Americans die every day from an opioid overdose (NIH 2018), and the labor market consequences are large enough to show up in national statistics – Kruger (2017) claims that opioid use accounts for 20 percent of the decline in male labor force participation and 25 percent of the decline in female labor force participation from 1999 to 2015. All told, Birnbaum et al. (2011) estimated that the social costs associated with opioid abuse were around $55 billion a year. Government officials frequently refer to the problem as an epidemic or a crisis.

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states in the evolution of opioid abuse rates to study the relationship between opioid misuse and crime. In particular, we use panel data to employ inference techniques that are robust to the possibility of endogeneity. Our most robust estimate is that each 1 percent increase in nonmedical use of pain relievers among 12-17 year olds is associated with an additional 36 property crimes per 100,000 residents, which is more than 1 percent of the mean property crime rate. A significant association also exists for opioid misuse among 18-25 year olds, although the association is weaker for users who are 26 years and older. The relationship holds across multiple identified sub-classes of property crime. Thus, although national statistics do not immediately present an obvious connection between opioid abuse and an increase in property crime, variation in their movements at the state level allow us to identify one. We also present evidence of a link between opioid prescriptions and property crime.

A number of falsification exercises suggest that this result is not merely a general correlation between drug use and crime. Evidence of an association between property crime and the use of other kinds of drugs is less robust, and there is no statistically significant relationship between any kind of drug use and violent crime. In particular, the connection between marijuana and crime is much less robust.

The paper proceeds as follows. Section II briefly reviews the literature on the connection between drugs and crime, with a focus on opioids. Section III presents the data and methods. Section IV presents the results and section V concludes.

II. Related Literature

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economic channel – drug users commit crimes for money to purchase drugs. Finally, the business of exchanging illicit drugs leads to crimes, mainly owing to the large economic rents available.2

Empirical work stemming from this conceptual framework is extensive. MacCoun et al. (2003) provide a review of what was known at that time, but conclude that much remains unknown – While “many different data sources establish a raw correlation between drug use and other criminal offenses,” the channel of causality is unclear, the effects are probabilistic with potentially small effect sizes, and the effect can vary across situations and over time even for the same person. An extensive meta-analysis by Bennett et al. (2008) documents studies that relate the use of many types of drugs to a variety of crimes and that use a host of different research designs. Like MacCoun et al. (2003), the authors conclude that the case for a drugs-crime correlation is convincing, but that causal channels remain unclear; they specifically cite the lack of a control group as a major drawback of many existing studies, most of which sample from offenders or drug users. Bennett and Edwards (2016) provide a more recent review of the literature. They document extensive variation by type of drug and by demographic characteristics of offenders, and also discuss some new evidence on causal paths, mainly gleaned from interviews with arrestees. The primacy on reliable causal inference in all of this research is worth emphasizing. As Bennett and Edwards (2016) put it:

If [drugs and crime] are not causally connected, then the reason for studying the connection and tackling the relationship through government interventions becomes less relevant. The strongest evidence of a drugs-causes-crime connection would be proving that when drug use goes up, crime goes up.

From a public policy perspective, an association has little relevance if policies that create changes in the control variable do not lead to changes in the outcome variable. Thus, new paths for exploring the nature of the relationship between drugs and crime are always of interest to researchers.

We now turn specifically to opioids. Opioids are a class of substances that are synthesized from the opium poppy plant and have strong pain-relieving effects. Unfortunately, these substances are highly addictive. Many users develop tolerance quickly, requiring larger doses to maintain the same level of pain

2_{Many economists would be quick to point out that the second and third problems are largely created by the fact that}

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relief. Furthermore, many users become physically dependent and experience severe withdrawal symptoms when they discontinue use. Shah et al. (2017) estimate that 20 percent of patients with a 10-day supply of opioids and 45 percent of patients with a 30-day supply become long-term users.

Within the framework of the model in Goldstein (1985), the strong addictiveness properties of opioids give us good reason to believe that their effect on property crime could be large. Before discussing evidence on this connection, we note that much of the literature intermingles heroin and prescription opioids. While distinct substances, their physiological effects are similar and about 80 percent of heroin users began by misusing prescription opioids (NIH 2018). Synthetic opioids like fentanyl are also quickly emerging and create similar social costs.

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Associations with other crimes and with other types of drugs are not significant.3_{Gropper (1985) is slightly}

different methodologically, in the sense that he compares the same individuals in periods of drug use and non-use, and finds that heroin users commit four to six times more crimes during periods of active use. A final strategy is to compare opioid addicts who are being treated with addicts who are not being treated. The effectiveness of treatment remains an open question. Hunt et al. (1984) and Havnes (2015), using data from the US and from Scandinavia respectively, find that opioid users in treatment are less likely to commit property crimes. By contrast, Pierce (2016) finds no effect of treatment on the risk of future offending.

We refer interested readers to Hayhurst et al. (2017) for a detailed discussion of what we can and cannot learn from cross-section studies along these lines. They conclude that “there is a surprising lack of robust evidence focusing specifically on pathways through opiate use and offending”, mainly because existing evidence is either old or is plagued with methodological problems.

Given this gap in the literature, it is surprising that more researchers have not used longitudinal data to investigate the connection between the prevalence of drug use and criminal activity. Degenhardt et al. (2005) find that increases in heroin prices in Australia were associated with increases in property crime and Baumer et al. (1998) find that increases in crack cocaine use were associated with reductions in burglary, but an uptick in robberies. The use of panel methods is well-established in research on other types of criminal activity. See, for example, Finkelhor and Jones (2006) and Paxson and Waldfogel (2003) on the relationship between movements in child maltreatment rates and various social and policy indicators.

In contrast to the findings from the cross-section studies mentioned above, Szalavitz and Rigg (2017) assert that there is a “curious disconnection between the opioid epidemic and crime” based on a number of casual observations from the data – Crime rates are not generally rising, the highest crime rates are in cities whereas opioid dependence is more of an issue in rural areas, and relatively few arrestees in the US test positive for opioids. The authors attribute this purported lack of association, in contrast to crime accompanying earlier drug epidemics, to two kinds of factors. First, the demographics are different. Opioid

3_{The effects are not uniform across demographics. Age of initiation matters (Nurco et al. 1993; Farabee et al. 2001),}

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users are more likely to be older, female and white than users of other drugs. Second, the supply chain for opioids is different. The drugs are initially produced by licensed manufacturers and small-scale exchanges facilitated by the Internet have, to a large degree, displaced street trade controlled by gangs.

In summary, there remains considerable uncertainty on the existence and magnitude of the relationship between opioid use and criminal activity, despite the fact that this is an issue of considerable policy relevance. Birnbaum (2011) estimated the social cost of opioids to be around $55 billion a year, and Hansen and Oster (2011) obtained a nearly identical estimate of $53.4 billion. The bulk of these social costs are productivity losses, but Birnbaum (2011) attributed 12.2 percent of the social costs to crime, and Hansen and Oster (2011) attributed 15 percent of the costs to crime. Thus, according to these studies, the social cost of crime stemming from the opioid epidemic is on the order of $7-$8 billion a year. Birnbaum (2011) specifically estimated property losses at around $625 million.

III. Data and Methods

The data are a state-level panel on drug use and crime from 2005 to 2014, including all 50 states and the District of Columbia. The years included span the entire range of data that are available at the state level on the opioid misuse variable that we use in this study.

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feeling of experience the drug caused” (SAMHSA 2013). Rates of misuse are available at the state level for three age groups – ages 12-17, ages 18-25 and ages 26 and above. Across states for the years sampled, the mean rate of prescription opioid misuse is 6.37 percent for ages 12-17, 11.39 percent for ages 18-25 and 3.46 percent for ages 26 and above. However, there is high variation. In 2007, the estimate in Arkansas was over 18 percent for 18-25 year olds. As a complement to our analysis of opioid abuse, we examine prescriptions of opioids, with data drawn from the IQVIA Xponent database. The mean rate across states is 79.41 prescriptions annually per 100 residents. Complete summary statistics are given in Table 1.

We also use the NSDUH to estimate rates of cocaine and marijuana use at the state level. The rates of cocaine use are lower, averaging 1.10 percent, 5.54 percent and 1.46 percent for our three age categories, respectively. For marijuana, the averages across states are 13.81 percent, 30.28 percent and 7.98 percent. Like the opioid question, these questions ask respondents about use over the previous year.

Our dependent variables are measures of the crime rate at the state level, taken from the FBI’s Uniform Crime Reporting (UCR) database, which aggregates data from local law enforcement agencies. We examine both property crimes and violent crimes. Property crimes are crimes that are committed with the objective of taking money or property, but with no violence or threat of violence. The database disaggregates property crimes into burglary, larceny and motor vehicle theft.4_{Rates are given as the number}

of crimes per 100,000 residents. The mean across states over our panel is 3004 property crimes per 100,000 residents, but again there is high variation – from a minimum of 1551 property crimes to a maximum of 5175 property crimes. For burglaries, the mean across states is 656 burglaries per 100,000 residents, with a mean of 2083 larcenies and 265 motor vehicle thefts. As for violent crimes, the average across states is 400 violent crimes per 100,000 residents, and this series features even higher levels of variation.

Our other control variables are designed to account for state-level factors that explain crime and that might also be correlated with drug use. We use a set of economic controls (the unemployment rate, the

4_{The FBI defines burglary as “the unlawful entry of a structure to commit a felony or theft”. By contrast, larceny is}

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poverty rate and income per capita), demographic controls (percent of residents who are male, percent of residents who are nonwhite), educational and labor market controls (percent of residents aged 25 and over with a high school diploma, percent of residents in blue-collar jobs) and health controls designed as proxies for risky behavior (percent of adults who drink heavily, percent of adults who are obese, percent of adults who are overweight). Table 1 summarizes all variables used in this study, including descriptions, summary statistics and data sources.

<<INSERT TABLE 1 HERE>>

The data comprise a panel of states observed in multiple years, indexed by 𝑖𝑖 and 𝑡𝑡 respectively. The simplest approach is to simply treat each state-year as a single observation:

𝐶𝐶𝐶𝐶𝑖𝑖𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 =𝛽𝛽0+𝛽𝛽1⋅ 𝐷𝐷𝐶𝐶𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡+𝛽𝛽2⋅ 𝑋𝑋𝑖𝑖,𝑡𝑡+𝐶𝐶𝑖𝑖,𝑡𝑡

Here, 𝐶𝐶𝐶𝐶𝑖𝑖𝐶𝐶𝐶𝐶_𝑖𝑖_,_𝑡𝑡 is a measure of state i’s crime rate at time t, and 𝐷𝐷𝐶𝐶𝐷𝐷𝐷𝐷_𝑖𝑖_,_𝑡𝑡 is the rate of drug use. 𝑋𝑋_𝑖𝑖_,_𝑡𝑡 is a vector of controls. This specification is likely to suffer from serious endogeneity problems owing to omitted variables bias. Unobserved characteristics at the state level that drive crime rates could presumably be correlated with drug use, and so it is difficult to ascribe any causal interpretation to the coefficient 𝛽𝛽₁.

Given the availability of panel data, a better option is a fixed-effects estimator:

𝐶𝐶𝐶𝐶𝑖𝑖𝐶𝐶𝐶𝐶𝑖𝑖,𝑡𝑡 =𝛽𝛽0+𝛽𝛽1⋅ 𝐷𝐷𝐶𝐶𝐷𝐷𝐷𝐷𝑖𝑖,𝑡𝑡+𝛽𝛽2⋅ 𝑋𝑋𝑖𝑖,𝑡𝑡+𝛼𝛼𝑖𝑖+𝐶𝐶𝑖𝑖,𝑡𝑡

The idea is that the fixed effects dummies 𝛼𝛼_𝑖𝑖 can control for unobserved differences across states, at least to the extent that these differences are constant over time. Intuitively, the fixed-effects estimator identifies the impact of drug use on crime based on changes in drug use and changes in crime rates within states, rather than comparing levels across states. We also incorporate dummies 𝛿𝛿_𝑡𝑡 for time fixed effects.

Throughout, all regressions are weighted by state population.

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variable in the regression as an error-correction term. Arellano and Bond (1991) proposed an estimator for this setting that has become standard in the literature. In essence, the estimator uses first-differences in the regression equation to capture the fixed effects (which corrects for static endogeneity), but it also uses lags of the dependent variable from several periods back as instruments, which corrects for endogeneity even that appears dynamically.5_{The Arellano-Bond estimator thus allows not only for fixed effects across states}

but, assuming that the instruments are properly specified, admits the possibility of a dynamic dependent variable that depends on its own history and further allows for independent variables that are endogenous and potentially correlated with current and past errors. This is likely to be the case in our setting given the complexities of the relationship between drugs and crime – Levels of drug use and even changes in drug use within states might be correlated with unobserved factors that are related to crime.

IV. Results

Identification for the fixed-effects and the Arellano-Bond models relies on temporal variation in opioid abuse within states, so we begin by describing this variation. We regress opioid misuse on a panel of state fixed effects and on a panel of both state and year fixed effects; the residuals from these regressions essentially capture the variation at the state level that we are using to identify the association between opioid misuse and crime. Figure 1 shows the variance in these residuals by state. Among the two younger age groups, where we ultimately obtain significant results, there are a number of states with high variance in opioid misuse across years. In other words, the result is not unduly influenced by a few states that exhibit large temporal swings. Figure 2 plots all residuals by state, with states sorted in order from lowest to highest mean squared residual across years. Again, there is substantial variation in many states.

<<INSERT FIGURE 1 HERE>> <<INSERT FIGURE 2 HERE>>

5_{The regression equation of interest is}_𝑦𝑦

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Capitalizing on this variation, we now proceed to our regression analysis. The main results are given in Table 2, which show regressions of property crime on opioid use among 18-25 year olds, typically the prime age for criminal activity. Table 3 shows results for opioid use among other age groups.6_Using

the estimate from our Arellano-Bond dynamic panel model, each 1 percent increase in opioid use among 18-25 year olds in a given state is associated with 24.64 additional property crimes per 100,000 residents. Each 1 percent increase in opioid use among 12-17 year olds is associated with 36.22 additional property crimes per 100,000 residents. Both coefficients are significant at the 1 percent level. Furthermore, the magnitude of the effect size is economically significant. A one-standard deviation increase in opioid use among both age groups is associated with 107.17 additional property crimes per 100,000 residents, which represents a 3.6 percent increase over the average rate of property crimes. The corresponding coefficient for opioid use among individuals 26 and older is only marginally significant. As for the other control variables, an increase in the percentage of residents with a high school diploma is associated with a significant reduction in the rate of property crime, and an increase in the percentage of residents who drink heavily is associated with a significant increase in property crime. Results for our economic variables are mixed – the signs of the coefficients suggest that higher poverty is associated with an increase in property crimes, but that higher unemployment and lower incomes are associated with fewer property crimes. However, none of the coefficients is significant and, in any case, these findings are not unusual. The connection between economic conditions and crime is complex (Levitt 2004).

<<INSERT TABLE 2 HERE>> <<INSERT TABLE 3 HERE>>

The contrast between the estimates obtained using our dynamic panel estimator and the estimates obtained using OLS estimators is informative in our context. In particular, the contrast cautions us about

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the threat of endogeneity in interpreting correlations between drugs and crime. In a straight OLS model with no dynamic panel effects and no fixed effects, the estimated coefficient is almost five times larger than the coefficient obtained using the dynamic panel estimator, for opioid use among 18-25 year olds. Specifically, the OLS estimate implies 116 additional property crimes per 100,000 residents associated with each 1 percent increase in opioid misuse, rather than the additional 25 property crimes implied by our dynamic panel estimator. In this case, the problem is clear and it is obvious that the former estimate is unreliable – Without fixed effects, there is a strong possibility that unobserved differences between states (e.g. the prevalence of undiagnosed mental health issues) are correlated with opioid use and also have an effect on crime. The fixed effects estimates are also biased, compared to the Arellano-Bond dynamic panel estimates, and the coefficient is not significant at conventional levels for the model incorporating both state and year fixed effects. In summary, the use of dynamic effects, as described in the previous section, appears to have significant import in estimating the relationship between drug use and crime.

Results on the relationship between opioid prescriptions and property crime are given in Table 4, and are qualitatively similar. Using the dynamic panel estimator, each additional opioid prescription (per 100 residents annually) is associated with 3.13 additional property crimes, and the coefficient is significant at the 5 percent level. The OLS estimates in this model also show substantial bias.

We now turn to the question of whether there is a unique connection between opioids and property crime, as we argued in section II that there might be, or whether all kinds of drugs are correlated with all kinds of crime. Table 5 shows regressions of the property crime rate on the use of other types of drugs.7

We focus on the Arellano-Bond estimates in the last column. Cocaine and marijuana use among 12-17 year olds displays a significant, positive association with property crime rates. However, cocaine and marijuana

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use among 18-25 year olds, the highest-crime age demographic, has no significant association with property crime rates after dynamic panel effects are taken into account. OLS estimates are biased in these regressions as well, and for marijuana use are not directionally consistent with the Arellano-Bond estimates.

We have investigated the relationship between opioid and other drug use with property crime. What about violent crime? Table 6 reports estimates from similar regressions, but regressing violent crime, rather than property crime, on opioid use. Using the Arellano-Bond estimates, there are no significant associations between violent crime and misuse of opioids among any age group. Again, OLS estimates are biased and appear to show a significant, positive association. Together with the results in Table 5, these results tell us that the association that we have identified is not merely a general linkage between drugs and crime. There is something about property crimes and about opioid use specifically that does not hold for violent crimes or for other drugs. We do not report them here, but results also show no significant positive association between violent crime and cocaine or marijuana.

Finally, having established an association between property crimes and opioid use, we investigate the types of property crimes that appear to be related to opioid use. The results in Table 7 show estimates from regressing all three identified sub-classes of property crime on opioid use. Again using the dynamic panel estimator, opioid use among 12-17 year olds and 18-25 year olds displays a positive and significant association with burglary and larceny, although not with motor vehicle theft.

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Tables 2-7 report standard regression diagnostics. For all of our dynamic panel instrumental variables models, the Sargan-Hansen test fails to reject the null hypothesis that the overidentifying restrictions are properly specified.8_{Arellano-Bond tests consistently find autocorrelation of the first order,}

as expected, but do not find autocorrelation of the second order.9_{This is additional evidence that OLS}

models are inappropriate and our tests show that use of first-differences, as in the fixed effects and dynamic panel models, eliminates the autocorrelation. Finally, the relatively low 𝑅𝑅2 in the models without fixed

effects shows that there is substantial variation in criminal activity that remains unexplained by our controls.

V. Conclusion

The literature is largely in agreement that there is some kind of connection between drug use and crime. However, the nature of this linkage remains elusive, and authors over the decades have developed a variety of strategies for parsing out the causal relationship more carefully.

Our paper contributes to this literature by bringing a new set of statistical tools to bear on the problem. Using dynamic panel analysis, we show that rates of prescription opioid misuse at the state level are associated with an increase in the number of property crimes. The magnitude is economically significant. Each 1 percent increase in the rate of misuse among 12-17 year olds is associated with 36.2 additional property crimes per 100,000 residents and, among 18-25 year olds, with 24.6 additional property crimes per 100,000 residents. A back-of-the-envelope calculation suggests that even a 1 percent increase in the rate of opioid misuse among these groups is associated with around 190,000 excess property crimes nationwide over the course of a year. Intuition would suggest that this is actually an underestimate of the social cost. Many property crimes go unreported, especially if addicts begin by stealing from family and friends. There is also evidence that quite a bit of opioid misuse turns into heroin use eventually, and so these numbers do not reflect users who began by using prescription opioids but are currently using heroin.

8_{We use the more robust two-step test statistic. See Roodman (2007) for details.}

9_{For regressions of violent crime, p-values for the null hypothesis of no second-order autocorrelation are between}

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Falsification exercises show that the relationship is not as simple as a straightforward drugs-crime connection. Other types of drugs do not display the same correlation with property crime, and there is no evidence of an association between opioid use and violent crime. Given the nature of opioid addiction, it is unsurprising that there is a unique connection between opioid misuse and property crime. It is also worth highlighting, in view of the current debate regarding marijuana laws, that evidence of a link between marijuana and crime is much less robust than evidence on opioids. While we stop short of saying that our estimated coefficients give the true causal impact of drug use on crime, our use of dynamic panel analysis is a step in the right direction and gives a warning about the potential for bias owing to endogeneity.

Given the intensity of the national conversation surrounding the opioid crisis, our results have quite a bit of policy relevance. Policymakers are beginning to target overprescription of opioids by physicians when there may be safer alternatives available. Our results are particularly informative in the context of this discussion since we present direct evidence of an association between property crime and opioid prescriptions, not just opioid abuse. As one example, Governor Christie signed a law in Feburary 2017 that limits initial opioid prescriptions in New Jersey to five days and requires physicians to establish a “pain management treatment program” and an informed consent statement for patients who require longer prescriptions (O’Shea 2017). Another possibility is to increase the quality and reduce the cost associated with treatment. As many as 60 percent of drug users relapse, but a relapse does not necessarily mean that treatment has failed; more or different interventions may be required (McLellan et al. 2000). Finally, policymakers might consider investing additional resources in public awareness campaigns, at least to the extent that they are evidence-based and reduce rates of addiction.

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Sutherland, R., Sindicich, N., Barrett, E., Whittaker, E., Peacock, A., Hickey, S., & Burns, L. (2015). Motivations, substance use and other correlates amongst property and violent offenders who regularly inject drugs. Addictive Behaviors, 45, 207-213.

Swan, A. C., & Goodman-Delahunty, J. (2013). The relationship between drug use and crime among police detainees: Does gender matter?. International Journal of Forensic Mental Health, 12(2), 107-115.

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19 Table 1

SUMMARY STATISTICS

Variable Description Source Mean Std. Dev. Min Max

Opioid 12-17 % misused opioids in last year NSDUH 6.37 1.34 3.54 10.32

Opioid 18-25 % misused opioids in last year NSDUH 11.39 2.38 7.02 18.01

Opioid 26+ % misused opioids in last year NSDUH 3.46 0.63 2.05 6.20

Cocaine 12-17 % used cocaine in last year NSDUH 1.10 0.46 0.33 2.44

Cocaine 18-25 % used cocaine in last year NSDUH 5.54 1.80 1.90 10.51

Cocaine 26+ % used cocaine in last year NSDUH 1.46 0.54 0.58 5.25

Marijuana 12-17 % used marijuana in last year NSDUH 13.81 2.43 8.77 21.50

Marijuana 18-25 % used marijuana in last year NSDUH 30.28 6.13 16.81 46.99

Marijuana 26+ % used marijuana in last year NSDUH 7.98 2.50 3.70 17.69

Prescribing Prescriptions per 100 residents CDC 79.41 23.16 28.5 146.9

Unemployment % civilian unemployed BLS 6.44 2.20 2.6 13.7

Poverty % residents below poverty line ACS 14.07 3.31 7.1 24.2

Income Income per capita, 2012 dollars BEA 52,302 20,200 32,770 183,971

Diploma % 25+ with HS diploma ACS 86.50 3.52 77.9 92.4

Nonwhite % residents nonwhite ACS 22.57 13.68 3.39 75.39

Male % residents male ACS 49.30 0.80 46.88 52.56

Blue Collar % residents in blue collar jobs ACS 22.53 3.99 5.8 31.4

Heavy drinking % adults who drink heavily BRFSS 5.49 1.33 1.9 9.8

Obese % adults obese (BMI>30) BRFSS 27.08 3.48 17.8 35.9

Overweight % adults overweight (BMI>25) BRFSS 63.13 3.42 51.7 70.7

Property crime Property crimes per 100K residents UCR 3004 730 1551 5175

Violent crime Violent crimes per 100K residents UCR 400 203 103 1508

Burglary Burglaries per 100K residents UCR 656 231 257 1212

Larceny Larcenies per 100K residents UCR 2083 453 1177 4077

Motor vehicle Auto thefts per 100K residents UCR 265 173 41 1326

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20 Table 2

PROPERTY CRIME AND OPIOID USE (AGE 18-25)

OLS State FE State + year FE Arellano-Bond

Opioid 115.89***

(14.68) 19.66*** (7.43) 8.85 (7.23) 24.64*** (6.23) Unemployment -92.47***

(13.68) 32.70*** (7.89) 24.61** (12.24) -8.96 (7.00)

Poverty 61.78***

(15.76) -66.58*** (13.26) -12.53 (17.14) 7.56 (12.52) Log(income) -1202.68***

(207.78) -300.16 (281.17) 996.75*** (316.31) 234.72 (303.36)

Diploma -12.31

(12.36) -109.47*** (20.33) -49.36* (27.33) -46.91*** (16.75)

Nonwhite 20.94***

(3.68) 37.61*** (7.93) 45.67*** (7.50) 0.79 (9.72)

Male 218.98***

(46.21) -27.89 (63.74) 118.05* (70.36) -100.53** (44.43) Blue collar 4.76

(13.32) 102.56*** (16.40) 39.36** (18.14) 16.23 (12.97) Heavy drinking 19.19

(24.93) 19.51 (12.84) 6.87 (15.32) 33.05*** (9.90)

Obese -24.61

(24.91) -14.36 (10.67) 14.92 (10.07) -18.63** (8.19) Overweight 36.11

(25.28) -7.15 (9.90) -5.86 (9.10) 4.00 (7.76)

Constant 2421

(4061) 14790 (4009) -10752 (5187) 7016* (3720)

Observations 510 510 510 510

Regression stats 𝑅𝑅2= 0.42 𝑅𝑅2= 0.95 𝑅𝑅2= 0.96 SH: 𝑝𝑝= 0.82

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.56

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21 Table 3

PROPERTY CRIME AND OPIOID USE (OTHER AGES)

OLS State FE State + year FE Arellano-Bond Age 12-17

Opioid 302.47***

(26.92) 36.76** (15.54) -11.35 (15.76) 36.22*** (12.04)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.65 Age 26+

Opioid 205.07***

(50.42) -14.26 (22.93) -9.21 (22.05) 33.03* (18.72)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.60

Notes: Standard errors appear in parentheses. * indicates significance at 10%. ** indicates significance at 5%. *** indicates significance at 1%. OLS and fixed-effects regressions weighted by state population size. SH is the Sargan-Hansen test for overidentifying restrictions. The p-value is for the null hypothesis that the overidentifying restrictions are valid. AB is the Arellano-Bond test for autocorrelation. The p-values are for the null hypothesis of no autocorrelation of the first order and no autocorrelation of the second order, respectively. All regressions use the same control variables as in Table 2.

Table 4

PROPERTY CRIME AND OPIOID PRESCRIPTIONS

OLS State FE State + year FE Arellano-Bond Prescribing 19.08***

(1.79) 5.48*** (1.45) 1.97 (1.72) 3.13** (1.23)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.99

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22 Table 5

PROPERTY CRIME AND OTHER DRUGS

OLS State FE State + year FE Arellano-Bond Cocaine – Age 12-17

Cocaine 970.96***

(98.57) 216.92*** (47.46) 42.20 (55.82) 88.22** (43.03)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.64 Cocaine – Age 18-25

Cocaine 96.11***

(28.32) -16.07 (12.69) -0.32 (11.96) 5.20 (10.76)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.60 Cocaine – Age 26+

Cocaine 413.21***

(83.35) -31.59 (36.77) 13.44 (34.66) 1.83 (27.78)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.59 Marijuana – Age 12-17

Marijuana -0.96 (19.55) -23.05** (9.63) -11.75 (9.04) 21.25*** (7.67)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.54 Marijuana – Age 18-25

Marijuana -43.79*** (8.23) -29.46*** (4.80) -15.70*** (4.80) 2.88 (4.30)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.58 Marijuana – Age 26+

Marijuana -59.59*** (17.72) -23.90*** (8.94) 20.37** (9.60) 3.08 (8.02)

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23 Table 6

VIOLENT CRIME AND OPIOIDS

OLS State FE State + year FE Arellano-Bond Age 12-17

Opioids 41.86***

(5.76)

6.58** (2.73)

2.74 (3.00)

3.13 (2.18)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.08 Age 18-25

Opioids 14.44**

(3.06)

2.72** (1.31)

1.19 (1.38)

0.79 (1.19)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.08 Age 26+

Opioids 29.78***

(10.21)

6.83* (4.02)

8.73** (4.18)

-0.14 (3.34)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.08

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24 Table 7

CATEGORIES OF PROPERTY CRIME AND OPIOIDS

OLS State FE State + year FE Arellano-Bond Burglary and opioids – Age 12-17

Opioids 83.90***

(7.85) 18.94*** (4.67) 3.39 (4.55) 13.56*** (4.20)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.42 Burglary and opioids – Age 18-25

Opioids 34.11***

(4.22) 8.65*** (2.24) 3.40 (2.09) 9.64*** (2.24)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.33 Larceny and opioids – Age 12-17

Opioids 173.43***

(18.14) 14.59 (9.64) -3.78 (10.14) 27.84*** (8.44)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.52 Larceny and opioids – Age 18-25

Opioids 62.83***

(9.80) 7.97* (4.61) 4.85 (4.65) 17.51*** (4.40)

AB: 𝑝𝑝= 0.00; 𝑝𝑝= 0.42 Motor vehicle theft and opioids – Age 12-17

Opioids 45.14***

(5.25) 3.21 (4.57) -10.97** (4.68) -3.95 (2.68)

AB: 𝑝𝑝= 0.01; 𝑝𝑝= 0.58 Motor vehicle theft and opioids – Age 18-25

Opioids 18.95***

(2.78) 3.05 (2.18) 0.61 (2.16) -0.61 (1.44)

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25 Figure 1

VARIANCE OF OPIOID MISUSE RESIDUALS BY STATE

State FE State + year FE

Age 12-17

Age 18-25

Age 26+

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26 Figure 2

OPIOID MISUSE RESIDUAL PLOTS BY STATE

State FE State + year FE

Opioids.pdf

Prescription Opioid Misuse and Property Crime

McCaslin Giles,

[email protected]

West Chester University

Michael Malcolm,

_{[email protected]}

West Chester University

June 2019

Age 12-17

Age 18-25

Age 26+

Age 12-17

Age 18-25

Age 26+